Hybrid AI

Extracted Attributes

• Primary Goal

  Integration of specialized AI systems into broad, versatile intelligent systems

• Cognitive Basis

  Natural intelligence operating on symbolic and sub-symbolic levels

• Reasoning Types

  Induction, deduction, abduction, and analogy

• Alternative Name

  Hybrid intelligent system

• Hardware Enablers

  GPUs (Graphics Processing Units) and NPUs (Neural Processing Units)

• Core Methodologies

  Neuro-symbolic systems, Neuro-fuzzy systems, Evolutionary neural networks, Fuzzy expert systems

Timeline

• Microsoft CEO Satya Nadella discusses the commitment to Hybrid AI and the use of local models on PCs at a Davos fireside chat. (Source: Document 4e50eb82-56c2-4d20-910f-9a43912c1cd7)

  2024-01-16

• Publication of research exploring the integration of Deep Learning and hybrid models for large-scale model integration. (Source: Arxiv: 2410.09186v3)

  2024-10-09

Hybrid intelligent system

Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: Neuro-symbolic systems Neuro-fuzzy systems Hybrid connectionist-symbolic models Fuzzy expert systems Connectionist expert systems Evolutionary neural networks Genetic fuzzy systems Rough fuzzy hybridization Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods. From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years, there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) and now is the time for integration to create broad AI systems. Proponents of this approach are researchers such as Marvin Minsky, Ron Sun, Aaron Sloman, Angelo Dalli and Michael A. Arbib. An example hybrid is a hierarchical control system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning (even by hybrid wisdom). Intelligent systems usually rely on hybrid reasoning processes, which include induction, deduction, abduction and reasoning by analogy.

Web Search Results

• What is Hybrid AI? An Approach for Data Discovery

  What is Hybrid AI? An Approach for Data Discovery # What is Hybrid AI? An Approach for Data Discovery Hybrid AI is a method of combining machine learning, which uses statistical models to analyze data, and symbolic AI, which is semantic-based and provides insights into meaning. By using the strengths of each technique, an outcome can be achieved that is more powerful than would be achievable by either one alone. ## What is an example of Hybrid AI in action? [...] The ultimate goal is to allow computers to simulate, as closely as possible, the functioning of the human brain. Computers don't think like us--the human brain is vastly more complex, versatile, and adaptable than any machine. The brain has 40 different neurotransmitters. One neuron can connect to ten thousand others. There's a level of complexity that's unimaginable and cannot yet be matched by a computer. But hybrid AI provides the scalability of machine learning with the nuances of symbolic AI to allow us to simulate the brain’s performance more closely, and provide information the system can act upon. ## How are Knowledge Graphs and Information Architecture Related to Hybrid AI? [...] ## What is an example of Hybrid AI in action? Let’s start with an example. Imagine that a machine learning system is trained to identify defective products. It is shown hundreds or thousands of examples of defective and non-defective products. It can then accurately distinguish between good products and defective ones. This is one of many use cases where machine learning is effective, and may well be both faster and superior to human performance, after the system is trained. But once the defective product is identified, what next?

• Hybrid AI: Blending Human Expertise with ML - Tredence

  Hybrid AI: Blending Human Expertise with ML Hybrid AI in Action: Addressing Real-World Limitations of Traditional AI How Hybrid AI Enhances Human Decision-Making in High-Stakes Use Cases Core Building Blocks of Enterprise-Grade Hybrid AI Systems Real-World Applications of Hybrid AI in Retail, Healthcare, and Manufacturing Operationalizing Hybrid AI: From Machine Learning Outputs to Human-in-the-Loop Feedback Hybrid AI vs Fully Automated AI: When to Blend, When to Automate How to Implement Hybrid AI in Your Organization: A Step-by-Step Guide Challenges and Solutions in Hybrid AI Implementation Future of Enterprise Intelligence: Hybrid AI and GenAI Working Together Conclusion: Why Hybrid AI is Key to Scalable, Responsible AI in the Enterprise FAQs [...] Hybrid AI: Blending Human Expertise with ML Hybrid AI in Action: Addressing Real-World Limitations of Traditional AI How Hybrid AI Enhances Human Decision-Making in High-Stakes Use Cases Core Building Blocks of Enterprise-Grade Hybrid AI Systems Real-World Applications of Hybrid AI in Retail, Healthcare, and Manufacturing Operationalizing Hybrid AI: From Machine Learning Outputs to Human-in-the-Loop Feedback Hybrid AI vs Fully Automated AI: When to Blend, When to Automate How to Implement Hybrid AI in Your Organization: A Step-by-Step Guide Challenges and Solutions in Hybrid AI Implementation Future of Enterprise Intelligence: Hybrid AI and GenAI Working Together Conclusion: Why Hybrid AI is Key to Scalable, Responsible AI in the Enterprise FAQs ##### Like the blog [...] Armed with this implementation roadmap, your organization can seamlessly integrate human expertise and machine intelligence to build robust Hybrid AI workflows. The result is a system that continuously learns from expert feedback, adapts to evolving data, and delivers reliable, scalable insights that drive tangible business value. ## Challenges and Solutions in Hybrid AI Implementation Even the most robust Hybrid AI initiatives can stumble without careful planning. Below are four common challenges and proven practices to address them: ### Data Quality and Integration Challenge: Inconsistent or siloed data sources can lead to mismatches between model outputs and expert reviews. Solution:

• A Systematic Review on Hybrid AI Models Integrating Machine ...

  Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers from scalability and latency issues. Hybrid AI models, which integrate ML and FL techniques, have emerged as a promising solution to balance performance, privacy, and scalability in cybersecurity. This systematic review investigates the current landscape of hybrid AI models, evaluating their strengths and limitations across five key dimensions: accuracy, privacy preservation, scalability, explainability, and robustness. Findings indicate that hybrid models consistently [...] Table 3. Hybrid AI models. [...] collaborative IDS in cyber-physical systems. Despite these advancements, scalability, communication efficiency, and explainability remain open research challenges, underscoring the need for continuous innovation in hybrid AI frameworks for cybersecurity.

• Hybrid models combining explainable AI and traditional machine ...

  To address this challenge, the field of Explainable AI has emerged, focusing on developing new methods and techniques to improve the interpretability and explainability of machine learning models. This review paper aims to provide a comprehensive overview of the research exploring the combination of Explainable AI and traditional machine learning approaches, known as "hybrid models". This paper discusses the importance of explainability in AI, and the necessity of combining interpretable machine learning models with black-box models to achieve the desired trade-off between accuracy and interpretability. It provides an overview of key methods and applications, integration techniques, implementation frameworks, evaluation metrics, and recent developments in the field of hybrid AI models. [...] The paper also delves into the challenges and limitations in implementing hybrid explainable AI systems, as well as the future trends in the integration of explainable AI and traditional machine learning. Altogether, this paper will serve as a valuable reference for researchers and practitioners working on developing explainable and interpretable AI systems. [...] Post-Hoc Explainability, Intrinsic Explainability, Combined Models

• Deep Learning, Hybrid Models, and Large-Scale Model Integration

  In this article, we explored various learning algorithms and their significance across a wide range of applications. The core concepts of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), hybrid models, Explainable AI (XAI), and their real-world applications were reviewed. We discussed some important subsets of machine learning algorithms that can be applied to solve real-world problems involving classification, prediction, and segmentation. We also explored the architecture of Convolutional Neural Networks (CNNs) and how they can be integrated with ML algorithms to develop hybrid models. The paper further addressed the vulnerability of learning algorithms to adversarial noise and its impact on model performance. Additionally, we investigated the integration of